Overview

Dataset statistics

Number of variables28
Number of observations507
Missing cells6062
Missing cells (%)42.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.2 KiB
Average record size in memory198.3 B

Variable types

Categorical12
Numeric5
Unsupported4
Text5
Boolean2

Alerts

HEAD_OFFICE_ID has constant value "2"Constant
REC_TYPE has constant value "U"Constant
CIN_EXT_SALES_ID has constant value ""Constant
CRNCY_ID has constant value "1.0"Constant
CIN_CRNCY has constant value "1"Constant
CIN_CRCY_REG has constant value ""Constant
CIN_EMAIL has constant value ""Constant
CIN_IS_DEL has constant value "False"Constant
CCT_ID is highly overall correlated with CIN_ACCT_CODE and 3 other fieldsHigh correlation
CIN_ACCT_CODE is highly overall correlated with CCT_ID and 2 other fieldsHigh correlation
CIN_GL_CODE is highly overall correlated with CCT_ID and 2 other fieldsHigh correlation
CIN_GST_CODE is highly overall correlated with SRC_DB_ID and 1 other fieldsHigh correlation
CIN_ID is highly overall correlated with SRC_DB_ID and 1 other fieldsHigh correlation
CIN_IS_ACTV is highly overall correlated with CIN_STATUS_CODE and 2 other fieldsHigh correlation
CIN_STATUS_CODE is highly overall correlated with CIN_IS_ACTV and 2 other fieldsHigh correlation
CIN_TYPE_ID is highly overall correlated with CIN_GL_CODE and 1 other fieldsHigh correlation
SITE_ID is highly overall correlated with SRC_TYPE_IDHigh correlation
SRC_DB_ID is highly overall correlated with CCT_ID and 6 other fieldsHigh correlation
SRC_TYPE_ID is highly overall correlated with CCT_ID and 9 other fieldsHigh correlation
CIN_STATUS_CODE is highly imbalanced (52.0%)Imbalance
CCT_ID is highly imbalanced (65.5%)Imbalance
CIN_ACCT_CODE is highly imbalanced (76.1%)Imbalance
REC_TYPE has 280 (55.2%) missing valuesMissing
CIN_EXT_SALES_ID has 227 (44.8%) missing valuesMissing
CIN_GRP_ID has 507 (100.0%) missing valuesMissing
CIN_STATUS_CODE has 227 (44.8%) missing valuesMissing
CIN_TYPE_ID has 227 (44.8%) missing valuesMissing
CCT_ID has 227 (44.8%) missing valuesMissing
CRNCY_ID has 227 (44.8%) missing valuesMissing
SITE_ID has 227 (44.8%) missing valuesMissing
CINEMA has 227 (44.8%) missing valuesMissing
CIN_ACCT_CODE has 227 (44.8%) missing valuesMissing
CIN_CODE has 6 (1.2%) missing valuesMissing
CIN_CRNCY has 227 (44.8%) missing valuesMissing
CIN_CRCY_REG has 227 (44.8%) missing valuesMissing
CIN_EMAIL has 363 (71.6%) missing valuesMissing
CIN_GL_CODE has 227 (44.8%) missing valuesMissing
CIN_GRP_CODE has 507 (100.0%) missing valuesMissing
CIN_GST_CODE has 227 (44.8%) missing valuesMissing
CIN_HO_CODE has 102 (20.1%) missing valuesMissing
CIN_IS_ACTV has 166 (32.7%) missing valuesMissing
CIN_IS_DEL has 166 (32.7%) missing valuesMissing
CIN_LIC_ISSUE_DATE has 507 (100.0%) missing valuesMissing
CIN_LIC_NO has 507 (100.0%) missing valuesMissing
CIN_NAME_SHORT has 227 (44.8%) missing valuesMissing
CIN_ID has unique valuesUnique
TIMESTAMP has unique valuesUnique
CIN_GRP_ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
CIN_GRP_CODE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CIN_LIC_ISSUE_DATE is an unsupported type, check if it needs cleaning or further analysisUnsupported
CIN_LIC_NO is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-12-18 06:28:20.416855
Analysis finished2025-12-18 06:28:22.271781
Duration1.85 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

HEAD_OFFICE_ID
Categorical

Constant 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2
507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters507
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2507
100.0%

Length

2025-12-18T17:28:22.306715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.337517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2507
100.0%

Most occurring characters

ValueCountFrequency (%)
2507
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2507
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2507
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2507
100.0%

REC_TYPE
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing280
Missing (%)55.2%
Memory size4.1 KiB
U
227 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters227
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U227
44.8%
(Missing)280
55.2%

Length

2025-12-18T17:28:22.372745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.398663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
u227
100.0%

Most occurring characters

ValueCountFrequency (%)
U227
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U227
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U227
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U227
100.0%

SRC_DB_ID
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.280079
Minimum3
Maximum1102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-12-18T17:28:22.436542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median4
Q368
95-th percentile1074.7
Maximum1102
Range1099
Interquartile range (IQR)64

Descriptive statistics

Standard deviation255.64568
Coefficient of variation (CV)2.8316954
Kurtosis11.215068
Mean90.280079
Median Absolute Deviation (MAD)0
Skewness3.6023158
Sum45772
Variance65354.716
MonotonicityNot monotonic
2025-12-18T17:28:22.487701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4280
55.2%
68125
24.7%
382
 
0.4%
172
 
0.4%
162
 
0.4%
642
 
0.4%
152
 
0.4%
442
 
0.4%
662
 
0.4%
112
 
0.4%
Other values (85)86
 
17.0%
ValueCountFrequency (%)
31
 
0.2%
4280
55.2%
51
 
0.2%
61
 
0.2%
71
 
0.2%
81
 
0.2%
91
 
0.2%
101
 
0.2%
112
 
0.4%
121
 
0.2%
ValueCountFrequency (%)
11021
0.2%
11001
0.2%
10991
0.2%
10971
0.2%
10961
0.2%
10951
0.2%
10941
0.2%
10931
0.2%
10921
0.2%
10911
0.2%

SRC_TYPE_ID
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
1
280 
3
125 
2
102 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters507
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

Length

2025-12-18T17:28:22.536108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.565070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

Most occurring characters

ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)507
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1280
55.2%
3125
24.7%
2102
 
20.1%

CIN_ID
Real number (ℝ)

High correlation  Unique 

Distinct507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4477.8777
Minimum3
Maximum10803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-12-18T17:28:22.603941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile28.3
Q1129.5
median262
Q310670.5
95-th percentile10771.7
Maximum10803
Range10800
Interquartile range (IQR)10541

Descriptive statistics

Standard deviation5190.1118
Coefficient of variation (CV)1.1590562
Kurtosis-1.8727723
Mean4477.8777
Median Absolute Deviation (MAD)214
Skewness0.36562828
Sum2270284
Variance26937260
MonotonicityNot monotonic
2025-12-18T17:28:22.652433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31
 
0.2%
71
 
0.2%
111
 
0.2%
151
 
0.2%
191
 
0.2%
231
 
0.2%
271
 
0.2%
311
 
0.2%
351
 
0.2%
391
 
0.2%
Other values (497)497
98.0%
ValueCountFrequency (%)
31
0.2%
41
0.2%
51
0.2%
61
0.2%
71
0.2%
81
0.2%
91
0.2%
101
0.2%
111
0.2%
121
0.2%
ValueCountFrequency (%)
108031
0.2%
108011
0.2%
108001
0.2%
107991
0.2%
107981
0.2%
107971
0.2%
107951
0.2%
107941
0.2%
107931
0.2%
107911
0.2%

CIN_EXT_SALES_ID
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
280 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
280
55.2%
(Missing)227
44.8%

Length

2025-12-18T17:28:22.695031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.715967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CIN_GRP_ID
Unsupported

Missing  Rejected  Unsupported 

Missing507
Missing (%)100.0%
Memory size4.1 KiB

CIN_STATUS_CODE
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.7%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
A
251 
I
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters280
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowI
5th rowA

Common Values

ValueCountFrequency (%)
A251
49.5%
I29
 
5.7%
(Missing)227
44.8%

Length

2025-12-18T17:28:22.743383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.772654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a251
89.6%
i29
 
10.4%

Most occurring characters

ValueCountFrequency (%)
A251
89.6%
I29
 
10.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A251
89.6%
I29
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A251
89.6%
I29
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A251
89.6%
I29
 
10.4%

CIN_TYPE_ID
Real number (ℝ)

High correlation  Missing 

Distinct13
Distinct (%)4.6%
Missing227
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean5.3964286
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-12-18T17:28:22.796633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q310
95-th percentile11
Maximum15
Range14
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.1135306
Coefficient of variation (CV)0.76226908
Kurtosis-1.2419639
Mean5.3964286
Median Absolute Deviation (MAD)5
Skewness0.32233088
Sum1511
Variance16.921134
MonotonicityNot monotonic
2025-12-18T17:28:22.829165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1105
20.7%
652
 
10.3%
1146
 
9.1%
728
 
5.5%
1013
 
2.6%
57
 
1.4%
27
 
1.4%
85
 
1.0%
35
 
1.0%
124
 
0.8%
Other values (3)8
 
1.6%
(Missing)227
44.8%
ValueCountFrequency (%)
1105
20.7%
27
 
1.4%
35
 
1.0%
57
 
1.4%
652
10.3%
728
 
5.5%
85
 
1.0%
1013
 
2.6%
1146
9.1%
124
 
0.8%
ValueCountFrequency (%)
151
 
0.2%
144
 
0.8%
133
 
0.6%
124
 
0.8%
1146
9.1%
1013
 
2.6%
85
 
1.0%
728
5.5%
652
10.3%
57
 
1.4%

CCT_ID
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)1.8%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
177.0
235 
178.0
35 
1178.0
 
7
1179.0
 
2
1180.0
 
1

Length

Max length6
Median length5
Mean length5.0357143
Min length5

Characters and Unicode

Total characters1410
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row1180.0
2nd row177.0
3rd row177.0
4th row177.0
5th row177.0

Common Values

ValueCountFrequency (%)
177.0235
46.4%
178.035
 
6.9%
1178.07
 
1.4%
1179.02
 
0.4%
1180.01
 
0.2%
(Missing)227
44.8%

Length

2025-12-18T17:28:22.867106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.897010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
177.0235
83.9%
178.035
 
12.5%
1178.07
 
2.5%
1179.02
 
0.7%
1180.01
 
0.4%

Most occurring characters

ValueCountFrequency (%)
7514
36.5%
1290
20.6%
0281
19.9%
.280
19.9%
843
 
3.0%
92
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7514
36.5%
1290
20.6%
0281
19.9%
.280
19.9%
843
 
3.0%
92
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7514
36.5%
1290
20.6%
0281
19.9%
.280
19.9%
843
 
3.0%
92
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7514
36.5%
1290
20.6%
0281
19.9%
.280
19.9%
843
 
3.0%
92
 
0.1%

CRNCY_ID
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
1.0
280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters840
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0280
55.2%
(Missing)227
44.8%

Length

2025-12-18T17:28:22.933600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:22.957522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0280
100.0%

Most occurring characters

ValueCountFrequency (%)
1280
33.3%
.280
33.3%
0280
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1280
33.3%
.280
33.3%
0280
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1280
33.3%
.280
33.3%
0280
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1280
33.3%
.280
33.3%
0280
33.3%

SITE_ID
Real number (ℝ)

High correlation  Missing 

Distinct105
Distinct (%)37.5%
Missing227
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean204.83571
Minimum2
Maximum7318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-12-18T17:28:22.989417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q121.75
median47.5
Q376
95-th percentile101
Maximum7318
Range7316
Interquartile range (IQR)54.25

Descriptive statistics

Standard deviation1054.0644
Coefficient of variation (CV)5.1459013
Kurtosis42.387921
Mean204.83571
Median Absolute Deviation (MAD)26.5
Skewness6.6366174
Sum57354
Variance1111051.7
MonotonicityNot monotonic
2025-12-18T17:28:23.038961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148
 
1.6%
698
 
1.6%
168
 
1.6%
807
 
1.4%
227
 
1.4%
597
 
1.4%
236
 
1.2%
216
 
1.2%
156
 
1.2%
415
 
1.0%
Other values (95)212
41.8%
(Missing)227
44.8%
ValueCountFrequency (%)
22
 
0.4%
32
 
0.4%
45
1.0%
51
 
0.2%
61
 
0.2%
72
 
0.4%
83
0.6%
94
0.8%
102
 
0.4%
115
1.0%
ValueCountFrequency (%)
73181
 
0.2%
73172
 
0.4%
73073
0.6%
1243
0.6%
1232
 
0.4%
1015
1.0%
1001
 
0.2%
995
1.0%
983
0.6%
973
0.6%

CINEMA
Text

Missing 

Distinct280
Distinct (%)100.0%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
2025-12-18T17:28:23.137341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length23
Mean length16.171429
Min length5

Characters and Unicode

Total characters4528
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)100.0%

Sample

1st rowState Theatre
2nd rowGeorge Street Cafe Bar
3rd rowParramatta Gold Class
4th rowzInactive-Newcastle
5th rowHornsby
ValueCountFrequency (%)
delivery54
 
9.0%
vmax46
 
7.7%
gold27
 
4.5%
class27
 
4.5%
cafe13
 
2.2%
bar13
 
2.2%
street11
 
1.8%
city10
 
1.7%
north8
 
1.3%
innaloo8
 
1.3%
Other values (142)380
63.7%
2025-12-18T17:28:23.279577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a427
 
9.4%
e409
 
9.0%
317
 
7.0%
l283
 
6.2%
o275
 
6.1%
r269
 
5.9%
i261
 
5.8%
n222
 
4.9%
t209
 
4.6%
s138
 
3.0%
Other values (46)1718
37.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)4528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a427
 
9.4%
e409
 
9.0%
317
 
7.0%
l283
 
6.2%
o275
 
6.1%
r269
 
5.9%
i261
 
5.8%
n222
 
4.9%
t209
 
4.6%
s138
 
3.0%
Other values (46)1718
37.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a427
 
9.4%
e409
 
9.0%
317
 
7.0%
l283
 
6.2%
o275
 
6.1%
r269
 
5.9%
i261
 
5.8%
n222
 
4.9%
t209
 
4.6%
s138
 
3.0%
Other values (46)1718
37.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a427
 
9.4%
e409
 
9.0%
317
 
7.0%
l283
 
6.2%
o275
 
6.1%
r269
 
5.9%
i261
 
5.8%
n222
 
4.9%
t209
 
4.6%
s138
 
3.0%
Other values (46)1718
37.9%

CIN_ACCT_CODE
Categorical

High correlation  Imbalance  Missing 

Distinct38
Distinct (%)13.6%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
243 
712915
 
1
718923
 
1
720920
 
1
724915
 
1
Other values (33)
33 

Length

Max length6
Median length0
Mean length0.79285714
Min length0

Characters and Unicode

Total characters222
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)13.2%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
243
47.9%
7129151
 
0.2%
7189231
 
0.2%
7209201
 
0.2%
7249151
 
0.2%
7169151
 
0.2%
7339151
 
0.2%
7269151
 
0.2%
7109201
 
0.2%
7359131
 
0.2%
Other values (28)28
 
5.5%
(Missing)227
44.8%

Length

2025-12-18T17:28:23.326755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7129151
 
2.7%
7189231
 
2.7%
7209201
 
2.7%
7249151
 
2.7%
7169151
 
2.7%
7339151
 
2.7%
7269151
 
2.7%
7109201
 
2.7%
7359131
 
2.7%
7199231
 
2.7%
Other values (27)27
73.0%

Most occurring characters

ValueCountFrequency (%)
149
22.1%
941
18.5%
739
17.6%
532
14.4%
223
10.4%
314
 
6.3%
012
 
5.4%
65
 
2.3%
84
 
1.8%
43
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
149
22.1%
941
18.5%
739
17.6%
532
14.4%
223
10.4%
314
 
6.3%
012
 
5.4%
65
 
2.3%
84
 
1.8%
43
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
149
22.1%
941
18.5%
739
17.6%
532
14.4%
223
10.4%
314
 
6.3%
012
 
5.4%
65
 
2.3%
84
 
1.8%
43
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
149
22.1%
941
18.5%
739
17.6%
532
14.4%
223
10.4%
314
 
6.3%
012
 
5.4%
65
 
2.3%
84
 
1.8%
43
 
1.4%

CIN_CODE
Text

Missing 

Distinct407
Distinct (%)81.2%
Missing6
Missing (%)1.2%
Memory size4.1 KiB
2025-12-18T17:28:23.435661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length6
Mean length6.0219561
Min length2

Characters and Unicode

Total characters3017
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique404 ?
Unique (%)80.6%

Sample

1st row102370
2nd row104918
3rd row107917
4th row111915
5th row117915
ValueCountFrequency (%)
9993
 
18.3%
gtscin5
 
1.0%
maqrie3
 
0.6%
gc3
 
0.6%
n'maqrie2
 
0.4%
cg2
 
0.4%
4749152
 
0.4%
1389172
 
0.4%
1023701
 
0.2%
n'4119151
 
0.2%
Other values (395)395
77.6%
2025-12-18T17:28:23.591839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9649
21.5%
1477
15.8%
2293
9.7%
4271
9.0%
5236
 
7.8%
'228
 
7.6%
0197
 
6.5%
7191
 
6.3%
3141
 
4.7%
n117
 
3.9%
Other values (20)217
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)3017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9649
21.5%
1477
15.8%
2293
9.7%
4271
9.0%
5236
 
7.8%
'228
 
7.6%
0197
 
6.5%
7191
 
6.3%
3141
 
4.7%
n117
 
3.9%
Other values (20)217
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9649
21.5%
1477
15.8%
2293
9.7%
4271
9.0%
5236
 
7.8%
'228
 
7.6%
0197
 
6.5%
7191
 
6.3%
3141
 
4.7%
n117
 
3.9%
Other values (20)217
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9649
21.5%
1477
15.8%
2293
9.7%
4271
9.0%
5236
 
7.8%
'228
 
7.6%
0197
 
6.5%
7191
 
6.3%
3141
 
4.7%
n117
 
3.9%
Other values (20)217
 
7.2%

CIN_CRNCY
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
1
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters280
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1280
55.2%
(Missing)227
44.8%

Length

2025-12-18T17:28:23.639287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:23.666526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1280
100.0%

Most occurring characters

ValueCountFrequency (%)
1280
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1280
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1280
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1280
100.0%

CIN_CRCY_REG
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.4%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
280 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
280
55.2%
(Missing)227
44.8%

Length

2025-12-18T17:28:23.767963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:23.789891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CIN_EMAIL
Categorical

Constant  Missing 

Distinct1
Distinct (%)0.7%
Missing363
Missing (%)71.6%
Memory size4.1 KiB
144 

Length

Max length0
Median length0
Mean length0
Min length0

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
144
 
28.4%
(Missing)363
71.6%

Length

2025-12-18T17:28:23.815886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-18T17:28:23.839969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CIN_GL_CODE
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)5.4%
Missing227
Missing (%)44.8%
Infinite0
Infinite (%)0.0%
Mean915.79643
Minimum370
Maximum927
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2025-12-18T17:28:23.858842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile915
Q1915
median917
Q3920.25
95-th percentile922
Maximum927
Range557
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation32.881856
Coefficient of variation (CV)0.035905203
Kurtosis274.96048
Mean915.79643
Median Absolute Deviation (MAD)2
Skewness-16.507026
Sum256423
Variance1081.2165
MonotonicityNot monotonic
2025-12-18T17:28:23.892968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
915104
20.5%
92256
 
11.0%
91847
 
9.3%
91726
 
5.1%
91913
 
2.6%
9147
 
1.4%
9235
 
1.0%
9135
 
1.0%
9164
 
0.8%
9264
 
0.8%
Other values (5)9
 
1.8%
(Missing)227
44.8%
ValueCountFrequency (%)
3701
 
0.2%
9135
 
1.0%
9147
 
1.4%
915104
20.5%
9164
 
0.8%
91726
 
5.1%
91847
9.3%
91913
 
2.6%
9203
 
0.6%
9211
 
0.2%
ValueCountFrequency (%)
9271
 
0.2%
9264
 
0.8%
9243
 
0.6%
9235
 
1.0%
92256
11.0%
9211
 
0.2%
9203
 
0.6%
91913
 
2.6%
91847
9.3%
91726
5.1%

CIN_GRP_CODE
Unsupported

Missing  Rejected  Unsupported 

Missing507
Missing (%)100.0%
Memory size4.1 KiB

CIN_GST_CODE
Categorical

High correlation  Missing 

Distinct14
Distinct (%)5.0%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
ABN: 33 595 052 153
84 
ABN:
70 
ABN: 40 009 659 643
54 
ABN: 99 000 024 439
23 
ABN: 11 913 370 633
17 
Other values (9)
32 

Length

Max length19
Median length19
Mean length15.210714
Min length4

Characters and Unicode

Total characters4259
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowABN:
2nd rowABN: 99 000 024 439
3rd rowABN: 33 595 052 153
4th rowABN: 99 000 024 439
5th rowABN: 11 913 370 633

Common Values

ValueCountFrequency (%)
ABN: 33 595 052 15384
 
16.6%
ABN:70
 
13.8%
ABN: 40 009 659 64354
 
10.7%
ABN: 99 000 024 43923
 
4.5%
ABN: 11 913 370 63317
 
3.4%
ABN: 25 221 284 7446
 
1.2%
ABN: 75 934 743 4516
 
1.2%
ABN: 16 588 912 2555
 
1.0%
ABN: 19 018 690 0394
 
0.8%
ABN: 92 630 664 9793
 
0.6%
Other values (4)8
 
1.6%
(Missing)227
44.8%

Length

2025-12-18T17:28:23.935350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abn279
25.0%
3384
 
7.5%
59584
 
7.5%
05284
 
7.5%
15384
 
7.5%
4054
 
4.8%
00954
 
4.8%
65954
 
4.8%
64354
 
4.8%
9923
 
2.1%
Other values (39)260
23.3%

Most occurring characters

ValueCountFrequency (%)
834
19.6%
3431
10.1%
5426
10.0%
0385
9.0%
9321
 
7.5%
:279
 
6.6%
B279
 
6.6%
N279
 
6.6%
A279
 
6.6%
4193
 
4.5%
Other values (6)553
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)4259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
834
19.6%
3431
10.1%
5426
10.0%
0385
9.0%
9321
 
7.5%
:279
 
6.6%
B279
 
6.6%
N279
 
6.6%
A279
 
6.6%
4193
 
4.5%
Other values (6)553
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
834
19.6%
3431
10.1%
5426
10.0%
0385
9.0%
9321
 
7.5%
:279
 
6.6%
B279
 
6.6%
N279
 
6.6%
A279
 
6.6%
4193
 
4.5%
Other values (6)553
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
834
19.6%
3431
10.1%
5426
10.0%
0385
9.0%
9321
 
7.5%
:279
 
6.6%
B279
 
6.6%
N279
 
6.6%
A279
 
6.6%
4193
 
4.5%
Other values (6)553
13.0%

CIN_HO_CODE
Text

Missing 

Distinct405
Distinct (%)100.0%
Missing102
Missing (%)20.1%
Memory size4.1 KiB
2025-12-18T17:28:24.073658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length6
Mean length6.9333333
Min length4

Characters and Unicode

Total characters2808
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique405 ?
Unique (%)100.0%

Sample

1st row102370
2nd row104918
3rd row107917
4th row111915
5th row117915
ValueCountFrequency (%)
gtscin4
 
1.0%
gc3
 
0.7%
maqrie3
 
0.7%
n'maqrie2
 
0.5%
1079171
 
0.2%
n'4289151
 
0.2%
1179151
 
0.2%
1249151
 
0.2%
1259201
 
0.2%
n'4339151
 
0.2%
Other values (394)394
95.6%
2025-12-18T17:28:24.255096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1471
16.8%
9459
16.3%
2292
10.4%
4269
9.6%
5231
8.2%
'228
8.1%
0197
7.0%
7188
 
6.7%
3140
 
5.0%
N119
 
4.2%
Other values (17)214
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1471
16.8%
9459
16.3%
2292
10.4%
4269
9.6%
5231
8.2%
'228
8.1%
0197
7.0%
7188
 
6.7%
3140
 
5.0%
N119
 
4.2%
Other values (17)214
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1471
16.8%
9459
16.3%
2292
10.4%
4269
9.6%
5231
8.2%
'228
8.1%
0197
7.0%
7188
 
6.7%
3140
 
5.0%
N119
 
4.2%
Other values (17)214
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1471
16.8%
9459
16.3%
2292
10.4%
4269
9.6%
5231
8.2%
'228
8.1%
0197
7.0%
7188
 
6.7%
3140
 
5.0%
N119
 
4.2%
Other values (17)214
7.6%

CIN_IS_ACTV
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.6%
Missing166
Missing (%)32.7%
Memory size4.1 KiB
True
251 
False
90 
(Missing)
166 
ValueCountFrequency (%)
True251
49.5%
False90
 
17.8%
(Missing)166
32.7%
2025-12-18T17:28:24.284995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CIN_IS_DEL
Boolean

Constant  Missing 

Distinct1
Distinct (%)0.3%
Missing166
Missing (%)32.7%
Memory size4.1 KiB
False
341 
(Missing)
166 
ValueCountFrequency (%)
False341
67.3%
(Missing)166
32.7%
2025-12-18T17:28:24.301942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

CIN_LIC_ISSUE_DATE
Unsupported

Missing  Rejected  Unsupported 

Missing507
Missing (%)100.0%
Memory size4.1 KiB

CIN_LIC_NO
Unsupported

Missing  Rejected  Unsupported 

Missing507
Missing (%)100.0%
Memory size4.1 KiB

CIN_NAME_SHORT
Text

Missing 

Distinct279
Distinct (%)99.6%
Missing227
Missing (%)44.8%
Memory size4.1 KiB
2025-12-18T17:28:24.411216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length9
Median length9
Mean length7.875
Min length6

Characters and Unicode

Total characters2205
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique278 ?
Unique (%)99.3%

Sample

1st rowSTATET
2nd rowGEORGE BS
3rd rowPMATTA GC
4th rowNEWCAS
5th rowHORNSB
ValueCountFrequency (%)
ct56
 
12.3%
vm46
 
10.1%
gc27
 
5.9%
bs12
 
2.6%
george8
 
1.8%
cherms8
 
1.8%
inaloo8
 
1.8%
macart7
 
1.5%
loganh7
 
1.5%
pacfar7
 
1.5%
Other values (111)269
59.1%
2025-12-18T17:28:24.576595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A197
 
8.9%
175
 
7.9%
C172
 
7.8%
T165
 
7.5%
R149
 
6.8%
N145
 
6.6%
M143
 
6.5%
O132
 
6.0%
E105
 
4.8%
S102
 
4.6%
Other values (17)720
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A197
 
8.9%
175
 
7.9%
C172
 
7.8%
T165
 
7.5%
R149
 
6.8%
N145
 
6.6%
M143
 
6.5%
O132
 
6.0%
E105
 
4.8%
S102
 
4.6%
Other values (17)720
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A197
 
8.9%
175
 
7.9%
C172
 
7.8%
T165
 
7.5%
R149
 
6.8%
N145
 
6.6%
M143
 
6.5%
O132
 
6.0%
E105
 
4.8%
S102
 
4.6%
Other values (17)720
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A197
 
8.9%
175
 
7.9%
C172
 
7.8%
T165
 
7.5%
R149
 
6.8%
N145
 
6.6%
M143
 
6.5%
O132
 
6.0%
E105
 
4.8%
S102
 
4.6%
Other values (17)720
32.7%

TIMESTAMP
Text

Unique 

Distinct507
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2025-12-18T17:28:24.655028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters9126
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique507 ?
Unique (%)100.0%

Sample

1st row0x00000007A0BFE21A
2nd row0x00000007A0031758
3rd row0x0000000779D05A91
4th row0x0000000629B9F537
5th row0x00000006DAF9F47E
ValueCountFrequency (%)
0x00000007a0bfe21a1
 
0.2%
0x00000007a00317581
 
0.2%
0x0000000779d05a911
 
0.2%
0x0000000629b9f5371
 
0.2%
0x00000006daf9f47e1
 
0.2%
0x00000007f7c5db841
 
0.2%
0x0000000779d05a981
 
0.2%
0x00000006156d9b951
 
0.2%
0x00000006156d9b9a1
 
0.2%
0x0000000779d05a9a1
 
0.2%
Other values (497)497
98.0%
2025-12-18T17:28:24.765377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
04396
48.2%
7577
 
6.3%
x507
 
5.6%
6502
 
5.5%
D465
 
5.1%
1348
 
3.8%
5314
 
3.4%
B290
 
3.2%
9285
 
3.1%
F280
 
3.1%
Other values (7)1162
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)9126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04396
48.2%
7577
 
6.3%
x507
 
5.6%
6502
 
5.5%
D465
 
5.1%
1348
 
3.8%
5314
 
3.4%
B290
 
3.2%
9285
 
3.1%
F280
 
3.1%
Other values (7)1162
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04396
48.2%
7577
 
6.3%
x507
 
5.6%
6502
 
5.5%
D465
 
5.1%
1348
 
3.8%
5314
 
3.4%
B290
 
3.2%
9285
 
3.1%
F280
 
3.1%
Other values (7)1162
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04396
48.2%
7577
 
6.3%
x507
 
5.6%
6502
 
5.5%
D465
 
5.1%
1348
 
3.8%
5314
 
3.4%
B290
 
3.2%
9285
 
3.1%
F280
 
3.1%
Other values (7)1162
 
12.7%

Interactions

2025-12-18T17:28:21.572716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.745190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.998668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.179632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.371844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.618840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.836955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.033272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.217887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.413402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.662592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.871014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.067159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.254289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.450453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.705110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.910382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.101109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.290594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.488574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.750031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:20.949760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.137697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.328321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-18T17:28:21.526849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-18T17:28:24.803242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CCT_IDCIN_ACCT_CODECIN_GL_CODECIN_GST_CODECIN_IDCIN_IS_ACTVCIN_STATUS_CODECIN_TYPE_IDSITE_IDSRC_DB_IDSRC_TYPE_ID
CCT_ID1.0000.5970.5020.3030.0000.0000.0000.0630.0001.0001.000
CIN_ACCT_CODE0.5971.0000.0000.0000.0000.0000.0000.1020.1831.0001.000
CIN_GL_CODE0.5020.0001.0000.0000.3200.3130.3130.582-0.071NaN1.000
CIN_GST_CODE0.3030.0000.0001.0000.0340.1300.1300.0000.1381.0001.000
CIN_ID0.0000.0000.3200.0341.0000.1760.0340.1290.2140.6290.703
CIN_IS_ACTV0.0000.0000.3130.1300.1761.0000.9810.0000.0001.0000.770
CIN_STATUS_CODE0.0000.0000.3130.1300.0340.9811.0000.0000.0001.0001.000
CIN_TYPE_ID0.0630.1020.5820.0000.1290.0000.0001.000-0.044NaN1.000
SITE_ID0.0000.183-0.0710.1380.2140.0000.000-0.0441.000NaN1.000
SRC_DB_ID1.0001.000NaN1.0000.6291.0001.000NaNNaN1.0000.505
SRC_TYPE_ID1.0001.0001.0001.0000.7030.7701.0001.0001.0000.5051.000

Missing values

2025-12-18T17:28:21.837297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-18T17:28:21.929283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-18T17:28:22.131501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

HEAD_OFFICE_IDREC_TYPESRC_DB_IDSRC_TYPE_IDCIN_IDCIN_EXT_SALES_IDCIN_GRP_IDCIN_STATUS_CODECIN_TYPE_IDCCT_IDCRNCY_IDSITE_IDCINEMACIN_ACCT_CODECIN_CODECIN_CRNCYCIN_CRCY_REGCIN_EMAILCIN_GL_CODECIN_GRP_CODECIN_GST_CODECIN_HO_CODECIN_IS_ACTVCIN_IS_DELCIN_LIC_ISSUE_DATECIN_LIC_NOCIN_NAME_SHORTTIMESTAMP
02None413NaNA1.01180.01.096.0State Theatre1023701None370NoneABN:102370TrueFalseNoneNoneSTATET0x00000007A0BFE21A
12None417NaNA10.0177.01.014.0George Street Cafe Bar1049181919NoneABN: 99 000 024 439104918TrueFalseNoneNoneGEORGE BS0x00000007A0031758
22None4111NaNA7.0177.01.058.0Parramatta Gold Class1079171917NoneABN: 33 595 052 153107917TrueFalseNoneNonePMATTA GC0x0000000779D05A91
32None4115NaNI1.0177.01.091.0zInactive-Newcastle1119151915NoneABN: 99 000 024 439111915FalseFalseNoneNoneNEWCAS0x0000000629B9F537
42None4119NaNA1.0177.01.018.0Hornsby1179151915NoneABN: 11 913 370 633117915TrueFalseNoneNoneHORNSB0x00000006DAF9F47E
52None4123NaNA1.0177.01.053.0Blacktown1249151915NoneABN: 99 000 024 439124915TrueFalseNoneNoneBLKTWN0x00000007F7C5DB84
62None4127NaNA11.0177.01.089.0Kotara Vmax1259201None918NoneABN: 99 000 024 439125920TrueFalseNoneNoneKOTARA VM0x0000000779D05A98
72None4131NaNA7.0177.01.011.0Bondi Junction Gold Class1329171917NoneABN: 33 595 052 153132917TrueFalseNoneNoneBONDIJ GC0x00000006156D9B95
82None4135NaNA7.0177.01.021.0Castle Hill Gold Class1339171917NoneABN: 25 221 284 744133917TrueFalseNoneNoneCASTLE GC0x00000006156D9B9A
92None4139NaNA11.0177.01.019.0Burwood Vmax1349201918NoneABN: 11 913 370 633134920TrueFalseNoneNoneBURWOD VM0x0000000779D05A9A
HEAD_OFFICE_IDREC_TYPESRC_DB_IDSRC_TYPE_IDCIN_IDCIN_EXT_SALES_IDCIN_GRP_IDCIN_STATUS_CODECIN_TYPE_IDCCT_IDCRNCY_IDSITE_IDCINEMACIN_ACCT_CODECIN_CODECIN_CRNCYCIN_CRCY_REGCIN_EMAILCIN_GL_CODECIN_GRP_CODECIN_GST_CODECIN_HO_CODECIN_IS_ACTVCIN_IS_DELCIN_LIC_ISSUE_DATECIN_LIC_NOCIN_NAME_SHORTTIMESTAMP
4972U38210760NoneNaNNoneNaNNaNNaNNaNNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNoneNone0x000000051251CE85
4982None4110764NaNA6.0177.01.07307.0Ed Square Delivery1279221None922NoneABN:127922TrueFalseNoneNoneEDPARK CT0x0000000779D05B23
4992None4110768NaNA6.0177.01.097.0Palmerston Delivery4839221None922NoneABN: 33 595 052 153483922TrueFalseNoneNonePALMNT CT0x0000000779D05B25
5002None4110772NaNA11.0178.01.075.0Queensgate Vmax7199187199201None918NoneABN:719920TrueFalseNoneNoneQSGATE VM0x0000000779D05B29
5012None4110776NaNA13.0177.01.040.0Strathpine Recline4709241None924NoneABN: 75 934 743 451470924TrueFalseNoneNoneSTRPNE RC0x0000000779D05B2C
5022None4110780NaNA14.0177.01.023.0Robina SCREENX4079261None926NoneABN: 40 009 659 643407926TrueFalseNoneNoneROBINA SX0x00000007D82F1BC3
5032None4110785NaNA10.0177.01.07317.0IMAX Sydney Cafe Bar1209181None919NoneABN:120918TrueFalseNoneNoneIMAXSY BS0x0000000779D05B2F
5042None4110791NaNA3.0177.01.022.0Pacific Fair IMAX4059234059231None923NoneABN: 40 009 659 643405923TrueFalseNoneNonePACFAR IM0x00000007D810F1C1
5052None4110797NaNA13.0177.01.029.0Rockhampton Recline4269241None924NoneABN: 40 009 659 643426924TrueFalseNoneNoneROCNTH RC0x0000000779D05B33
5062None4110801NaNA14.0177.01.059.0Loganholme SCREENX4509261None926NoneABN: 130 80111 173450926TrueFalseNoneNoneLOGANH SX0x00000007D82F1BC6